{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try to train different classification models on the data like Logistic Regression, k-nearest neighbors classifier, Random Forest and a Support Vector Machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import required libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Alkphos Alkaline Phosphotase</th>\n",
       "      <th>Sgpt Alamine Aminotransferase</th>\n",
       "      <th>Sgot Aspartate Aminotransferase</th>\n",
       "      <th>Total Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin-Globulin Ratio</th>\n",
       "      <th>Selector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>Male</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total Bilirubin  Direct Bilirubin  \\\n",
       "0   65  Female              0.7               0.1   \n",
       "1   62    Male             10.9               5.5   \n",
       "2   62    Male              7.3               4.1   \n",
       "3   58    Male              1.0               0.4   \n",
       "4   72    Male              3.9               2.0   \n",
       "\n",
       "   Alkphos Alkaline Phosphotase  Sgpt Alamine Aminotransferase  \\\n",
       "0                           187                             16   \n",
       "1                           699                             64   \n",
       "2                           490                             60   \n",
       "3                           182                             14   \n",
       "4                           195                             27   \n",
       "\n",
       "   Sgot Aspartate Aminotransferase  Total Protiens  Albumin  \\\n",
       "0                               18             6.8      3.3   \n",
       "1                              100             7.5      3.2   \n",
       "2                               68             7.0      3.3   \n",
       "3                               20             6.8      3.4   \n",
       "4                               59             7.3      2.4   \n",
       "\n",
       "   Albumin-Globulin Ratio  Selector  \n",
       "0                    0.90         1  \n",
       "1                    0.74         1  \n",
       "2                    0.89         1  \n",
       "3                    1.00         1  \n",
       "4                    0.40         1  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File does not contain headers so we need to load the headers manually\n",
    "features = [\"Age\", \"Gender\", \"Total Bilirubin\", \"Direct Bilirubin\", \"Alkphos Alkaline Phosphotase\", \"Sgpt Alamine Aminotransferase\", \"Sgot Aspartate Aminotransferase\", \"Total Protiens\", \"Albumin\", \"Albumin-Globulin Ratio\", \"Selector\"]\n",
    "data = pd.read_csv('ilpd.csv', names = features)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 11 columns):\n",
      "Age                                583 non-null int64\n",
      "Gender                             583 non-null object\n",
      "Total Bilirubin                    583 non-null float64\n",
      "Direct Bilirubin                   583 non-null float64\n",
      "Alkphos Alkaline Phosphotase       583 non-null int64\n",
      "Sgpt Alamine Aminotransferase      583 non-null int64\n",
      "Sgot Aspartate Aminotransferase    583 non-null int64\n",
      "Total Protiens                     583 non-null float64\n",
      "Albumin                            583 non-null float64\n",
      "Albumin-Globulin Ratio             579 non-null float64\n",
      "Selector                           583 non-null int64\n",
      "dtypes: float64(5), int64(5), object(1)\n",
      "memory usage: 50.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Overview of data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Albumin-Globulin Ratio feature has four missing values, as seen above. Here, we are dropping those particular rows which have missing data. We could, in fact, fill those place with values of our own, using options like: \n",
    "1. A constant value that has meaning within the domain, such as 0, distinct from all other values.\n",
    "2. A value from another randomly selected record, or the immediately next or previous record.\n",
    "3. A mean, median or mode value for the column.\n",
    "4. A value estimated by another predictive model.\n",
    "\n",
    "But here, since a very small fraction of values are missing, we choose to drop those rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Transfrom Gender string into float values\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(['Male','Female'])\n",
    "data.loc[:,'Gender'] = le.transform(data['Gender'])\n",
    "\n",
    "#Remove rows with missing values\n",
    "data = data.dropna(how = 'any', axis = 0)\n",
    "\n",
    "#Also transform Selector variable into usual conventions followed\n",
    "data['Selector'] = data['Selector'].map({2:0, 1:1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Alkphos Alkaline Phosphotase</th>\n",
       "      <th>Sgpt Alamine Aminotransferase</th>\n",
       "      <th>Sgot Aspartate Aminotransferase</th>\n",
       "      <th>Total Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin-Globulin Ratio</th>\n",
       "      <th>Selector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699</td>\n",
       "      <td>64</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490</td>\n",
       "      <td>60</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195</td>\n",
       "      <td>27</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  Total Bilirubin  Direct Bilirubin  \\\n",
       "0   65       0              0.7               0.1   \n",
       "1   62       1             10.9               5.5   \n",
       "2   62       1              7.3               4.1   \n",
       "3   58       1              1.0               0.4   \n",
       "4   72       1              3.9               2.0   \n",
       "\n",
       "   Alkphos Alkaline Phosphotase  Sgpt Alamine Aminotransferase  \\\n",
       "0                           187                             16   \n",
       "1                           699                             64   \n",
       "2                           490                             60   \n",
       "3                           182                             14   \n",
       "4                           195                             27   \n",
       "\n",
       "   Sgot Aspartate Aminotransferase  Total Protiens  Albumin  \\\n",
       "0                               18             6.8      3.3   \n",
       "1                              100             7.5      3.2   \n",
       "2                               68             7.0      3.3   \n",
       "3                               20             6.8      3.4   \n",
       "4                               59             7.3      2.4   \n",
       "\n",
       "   Albumin-Globulin Ratio  Selector  \n",
       "0                    0.90         1  \n",
       "1                    0.74         1  \n",
       "2                    0.89         1  \n",
       "3                    1.00         1  \n",
       "4                    0.40         1  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Overview of data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Total Bilirubin</th>\n",
       "      <th>Direct Bilirubin</th>\n",
       "      <th>Alkphos Alkaline Phosphotase</th>\n",
       "      <th>Sgpt Alamine Aminotransferase</th>\n",
       "      <th>Sgot Aspartate Aminotransferase</th>\n",
       "      <th>Total Protiens</th>\n",
       "      <th>Albumin</th>\n",
       "      <th>Albumin-Globulin Ratio</th>\n",
       "      <th>Selector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "      <td>579.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.782383</td>\n",
       "      <td>0.758204</td>\n",
       "      <td>3.315371</td>\n",
       "      <td>1.494128</td>\n",
       "      <td>291.366149</td>\n",
       "      <td>81.126079</td>\n",
       "      <td>110.414508</td>\n",
       "      <td>6.481693</td>\n",
       "      <td>3.138515</td>\n",
       "      <td>0.947064</td>\n",
       "      <td>0.715026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.221786</td>\n",
       "      <td>0.428542</td>\n",
       "      <td>6.227716</td>\n",
       "      <td>2.816499</td>\n",
       "      <td>243.561863</td>\n",
       "      <td>183.182845</td>\n",
       "      <td>289.850034</td>\n",
       "      <td>1.084641</td>\n",
       "      <td>0.794435</td>\n",
       "      <td>0.319592</td>\n",
       "      <td>0.451792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.500000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age      Gender  Total Bilirubin  Direct Bilirubin  \\\n",
       "count  579.000000  579.000000       579.000000        579.000000   \n",
       "mean    44.782383    0.758204         3.315371          1.494128   \n",
       "std     16.221786    0.428542         6.227716          2.816499   \n",
       "min      4.000000    0.000000         0.400000          0.100000   \n",
       "25%     33.000000    1.000000         0.800000          0.200000   \n",
       "50%     45.000000    1.000000         1.000000          0.300000   \n",
       "75%     58.000000    1.000000         2.600000          1.300000   \n",
       "max     90.000000    1.000000        75.000000         19.700000   \n",
       "\n",
       "       Alkphos Alkaline Phosphotase  Sgpt Alamine Aminotransferase  \\\n",
       "count                    579.000000                     579.000000   \n",
       "mean                     291.366149                      81.126079   \n",
       "std                      243.561863                     183.182845   \n",
       "min                       63.000000                      10.000000   \n",
       "25%                      175.500000                      23.000000   \n",
       "50%                      208.000000                      35.000000   \n",
       "75%                      298.000000                      61.000000   \n",
       "max                     2110.000000                    2000.000000   \n",
       "\n",
       "       Sgot Aspartate Aminotransferase  Total Protiens     Albumin  \\\n",
       "count                       579.000000      579.000000  579.000000   \n",
       "mean                        110.414508        6.481693    3.138515   \n",
       "std                         289.850034        1.084641    0.794435   \n",
       "min                          10.000000        2.700000    0.900000   \n",
       "25%                          25.000000        5.800000    2.600000   \n",
       "50%                          42.000000        6.600000    3.100000   \n",
       "75%                          87.000000        7.200000    3.800000   \n",
       "max                        4929.000000        9.600000    5.500000   \n",
       "\n",
       "       Albumin-Globulin Ratio    Selector  \n",
       "count              579.000000  579.000000  \n",
       "mean                 0.947064    0.715026  \n",
       "std                  0.319592    0.451792  \n",
       "min                  0.300000    0.000000  \n",
       "25%                  0.700000    0.000000  \n",
       "50%                  0.930000    1.000000  \n",
       "75%                  1.100000    1.000000  \n",
       "max                  2.800000    1.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#features characteristics to determine if feature scaling is necessary\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It clearly reflects from the above output that values across different variables are distributed far too extensively. We could perform feature scaling or normalization so as to improve the classifier accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data, data['Selector'], random_state = 0)\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive records: 414\n",
      "Negative records: 165\n"
     ]
    }
   ],
   "source": [
    "#Determining the healthy-affected split\n",
    "print(\"Positive records:\", data['Selector'].value_counts().iloc[0])\n",
    "print(\"Negative records:\", data['Selector'].value_counts().iloc[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This output confirms that we have 414 positive and 165 negative records. This indicates that this is a highly unbalanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0Y6sLAD5rbW3tqPfv3bs3F1xwwdzj7d+//3hL4mbu0Htu9L11JIt+zx3rMzHC5wFYJFui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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2573e4c1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determine statistics based on age\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.hist(data[data['Selector'] == 1]['Age'], bins = 16, align = 'mid', rwidth = 0.5, color = 'black', alpha = 0.8)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number of Patients')\n",
    "plt.title('Frequency-Age Distribution')\n",
    "plt.grid(True)\n",
    "plt.savefig('fig1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the age vs. frequency graph, we can observe that middle-aged people are the worst affected. Even elderly people are also suffering from liver ailments, as seen by the bar sizes at ages 60-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PYey4Idjb52HUiMmJlleqVJ7Zc6bQ8e3ehGTwmKDrxy7gWNwFB/O7qWKrupze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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2573e359780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation-matrix\n",
    "plt.subplots(figsize=(12, 10))\n",
    "plt.title('Pearson Correlation of Features')\n",
    "# Draw the heatmap using seaborn\n",
    "sns.heatmap(data.corr(),linewidths=0.25, vmax=1.0, square=True,annot=True)\n",
    "plt.savefig('fig2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation matrix gives us the relationship between two features. As seen above, the following pairs of features seem to be very closely related as indicated by their high correlation coefficients:\n",
    "1. Total Bilirubin and Direct Bilirubin(0.87)\n",
    "2. Sgpt Alamine Aminotransferase and Sgot Aspartate Aminotransferase(0.79)\n",
    "3. Albumin and Total Proteins(0.78)\n",
    "4. Albumin and Albumin-Globulin Ratio(0.69)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Using Classification Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now evaluate the performance of various classifiers on this dataset. For the sake of understanding as to how feature scaling affects classifier performance, we will train models using both scaled and unscaled data.\n",
    "Since we are interested in capturing records of people who have been tested positive, we will base our classifier evaluation metric on precision and recall instead of accuracy.\n",
    "We could also use F1 score, since it takes into account both precision and recall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classifier on unscaled test data:\n",
      "Accuracy: 0.993103448276\n",
      "Precision: 0.990476190476\n",
      "Recall: 1.0\n",
      "F-1 score: 0.995215311005\n"
     ]
    }
   ],
   "source": [
    "#Using normal data\n",
    "logreg = LogisticRegression(C = 0.1).fit(X_train, y_train)\n",
    "print(\"Logistic Regression Classifier on unscaled test data:\")\n",
    "print(\"Accuracy:\", logreg.score(X_test, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, logreg.predict(X_test)))\n",
    "print(\"Recall:\", recall_score(y_test, logreg.predict(X_test)))\n",
    "print(\"F-1 score:\", f1_score(y_test, logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Classifier on scaled test data:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F-1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#Using feature-scaled data\n",
    "logreg_scaled = LogisticRegression(C = 0.1).fit(X_train_scaled, y_train)\n",
    "print(\"Logistic Regression Classifier on scaled test data:\")\n",
    "print(\"Accuracy:\", logreg_scaled.score(X_test_scaled, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, logreg_scaled.predict(X_test_scaled)))\n",
    "print(\"Recall:\", recall_score(y_test, logreg_scaled.predict(X_test_scaled)))\n",
    "print(\"F-1 score:\", f1_score(y_test, logreg_scaled.predict(X_test_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well! The performance has definitely improved by feature scaling, though not drastically, as there was already very little scope of improvement.\n",
    "Let us look at other classifiers and analyse how they react to scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. SVM Classifier with RBF kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Classifier on unscaled test data:\n",
      "Accuracy: 0.71724137931\n",
      "Precision: 0.71724137931\n",
      "Recall: 1.0\n",
      "F-1 score: 0.835341365462\n"
     ]
    }
   ],
   "source": [
    "#Using normal data\n",
    "svc_clf = SVC(C = 0.1, kernel = 'rbf').fit(X_train, y_train)\n",
    "print(\"SVM Classifier on unscaled test data:\")\n",
    "print(\"Accuracy:\", svc_clf.score(X_test, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, svc_clf.predict(X_test)))\n",
    "print(\"Recall:\", recall_score(y_test, svc_clf.predict(X_test)))\n",
    "print(\"F-1 score:\", f1_score(y_test, svc_clf.predict(X_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Classifier on scaled test data:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F-1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#Using scaled data\n",
    "svc_clf_scaled = SVC(C = 0.1, kernel = 'rbf').fit(X_train_scaled, y_train)\n",
    "print(\"SVM Classifier on scaled test data:\")\n",
    "print(\"Accuracy:\", svc_clf_scaled.score(X_test_scaled, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, svc_clf_scaled.predict(X_test_scaled)))\n",
    "print(\"Recall:\", recall_score(y_test, svc_clf_scaled.predict(X_test_scaled)))\n",
    "print(\"F-1 score:\", f1_score(y_test, svc_clf_scaled.predict(X_test_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again perfect scores! This time, the performance improvement is quite noticeable, as there is a rise of almost 0.18 in the F-1 score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. K-NN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k-NN Classifier on unscaled test data:\n",
      "Accuracy: 0.613793103448\n",
      "Precision: 0.703389830508\n",
      "Recall: 0.798076923077\n",
      "F-1 score: 0.747747747748\n"
     ]
    }
   ],
   "source": [
    "#Using normal data\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train, y_train)\n",
    "print(\"k-NN Classifier on unscaled test data:\")\n",
    "print(\"Accuracy:\", knn.score(X_test, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, knn.predict(X_test)))\n",
    "print(\"Recall:\", recall_score(y_test, knn.predict(X_test)))\n",
    "print(\"F-1 score:\", f1_score(y_test, knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Classifier on scaled test data:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F-1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#Using scaled data\n",
    "knn_scaled = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn_scaled.fit(X_train_scaled, y_train)\n",
    "print(\"SVM Classifier on scaled test data:\")\n",
    "print(\"Accuracy:\", knn_scaled.score(X_test_scaled, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, knn_scaled.predict(X_test_scaled)))\n",
    "print(\"Recall:\", recall_score(y_test, knn_scaled.predict(X_test_scaled)))\n",
    "print(\"F-1 score:\", f1_score(y_test, knn_scaled.predict(X_test_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Classifier on unscaled test data:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F-1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#using normal data\n",
    "rfc = RandomForestClassifier(n_estimators = 20)\n",
    "rfc.fit(X_train, y_train)\n",
    "print(\"SVM Classifier on unscaled test data:\")\n",
    "print(\"Accuracy:\", rfc.score(X_test, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, rfc.predict(X_test)))\n",
    "print(\"Recall:\", recall_score(y_test, rfc.predict(X_test)))\n",
    "print(\"F-1 score:\", f1_score(y_test, rfc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier on scaled test data:\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F-1 score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#using scaled data\n",
    "rfc_scaled = RandomForestClassifier(n_estimators = 20)\n",
    "rfc_scaled.fit(X_train_scaled, y_train)\n",
    "print(\"Random Forest Classifier on scaled test data:\")\n",
    "print(\"Accuracy:\", rfc_scaled.score(X_test_scaled, y_test))\n",
    "print(\"Precision:\", precision_score(y_test, rfc_scaled.predict(X_test_scaled)))\n",
    "print(\"Recall:\", recall_score(y_test, rfc_scaled.predict(X_test_scaled)))\n",
    "print(\"F-1 score:\", f1_score(y_test, rfc_scaled.predict(X_test_scaled)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest Classifier works well either way, it seems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature scaling definitely was helpful in improving model performance. While the Random Forest Classifier performed equally well with and without feature scaling, it is not necessary that this may be the case always. For, performance issues with feature scaling depend on a lot of factors:\n",
    "1. Understanding the data distribution is important. It is highly possible that there may be some features which are almost constant except for a small noise-driven variation. This noise would then be amplified greatly by the normalization.\n",
    "2. The regularization parameter C is also a very important factor in classifier performance. This is crucial either way, whether or not feature scaling is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further applications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not expected to get the same level of performance on bigger and denser data sets. The main reasons behind this are:\n",
    "1. The dataset we worked on was very small, consisting of only 583 observations, of which 4 were dropped.\n",
    "2. The dataset was highly unbalanced, the postive records being three times the number of negative ones.\n",
    "\n",
    "Hence, even though we have obtained perfect scores on this dataset, the performance of the same models on similar but bigger datasets is expected to worsen."
   ]
  }
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